Mechanic of Advanced and Smart Materials
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Spatiotemporal Wake Learning via Three-Dimensional Convolution for Instantaneous Aerodynamic Load Estimation on an Elastically Mounted Square Cylinder

Articles in Press

Document Type : Original Article

Authors

Ahmadreza Rabiee 1
Amir Hossein Rabiee 2
Mostafa Esmaeili 3

1 Department of Computer Engineering, Faculty of Engineering, Arak University, Arak 38181-75846, Iran

2 School of Mechanical Engineering, Arak University of Technology, Arak 38181-41167, Iran

3 Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University, Tehran 15719-14911, Iran

10.66224/masm.2026.2105501.1205
Abstract
This study presents a unified three-dimensional convolutional neural network (3D CNN) for the direct prediction of the instantaneous lift coefficient in turbulent flow-induced vibration of a two-degree-of-freedom elastically mounted square cylinder. Physics-consistent data were generated using a validated two-way coupled URANS-FSI model with the k-ω SST turbulence closure. After excluding the initial transient response, 1017 synchronized wake-field snapshots and lift-coefficient values were collected over 101.7 s. Consecutive flow images were processed as a single spatio-temporal volume, allowing the Conv3D kernels to learn vortex morphology, shear-layer evolution, and short-term wake dynamics without recurrent units or reduced-order preprocessing. A chronological train-validation-test division was adopted to preserve temporal causality. Sensitivity analysis identified a sequence length of T = 18 and a batch size of 14 as the optimal configuration. The final network achieved R2train = 0.932 and R2test = 0.919, with approximate MAE = 0.19 and MSE = 0.06. Time-history comparisons showed accurate recovery of oscillation amplitude, extrema, zero crossings, and phase over the unseen test interval. These results demonstrate that direct three-dimensional convolution can provide an effective image-to-force surrogate for turbulent fluid-structure interaction.

Keywords

Flow-induced vibration
Three-dimensional convolutional neural network
Lift coefficient prediction
Fluid-structure interaction
Square cylinder
Mechanic of Advanced and Smart Materials

Articles in Press, Accepted Manuscript
Available Online from 11 October 2026

  • Receive Date 09 September 2026
  • Revise Date 07 October 2026
  • Accept Date 11 October 2026

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